New AI Framework Reads and Rewrites Cell Trajectories Inside Living Tissue

Every tissue in the body is a work in progress. Cells divide, mature, migrate, and change identity in response to signals from their neighbors, and understanding these transitions is one of the central quests of modern biology. For years, researchers have relied on single-cell RNA sequencing to catalog the states that cells occupy, but these measurements are fundamentally snapshots. They reveal what a cell looks like at the moment of capture, yet they say little about where that cell is heading. RNA velocity, introduced in 2018, offered a way around this limitation by comparing newly transcribed, unspliced messenger RNA with mature, spliced transcripts, allowing scientists to extrapolate each cell’s likely future state. The approach transformed how developmental biologists and cancer researchers think about cellular trajectories, but it has always carried a blind spot: it ignores the physical geography of tissue.

That blind spot matters more than many researchers realized. A cell’s fate is not determined solely by the genes it expresses; it is shaped by its position within a tissue, by the density of surrounding cells, by physical constraints, and by local signaling interactions. Conventional RNA velocity methods treat cells as independent entities floating in an abstract expression space, and the result is often inferred dynamics that contradict the actual architecture of the organ being studied. Trajectories may point across anatomical boundaries where no plausible transition exists, or flows may run in directions that make no sense given the known organization of, say, the cerebral cortex. Now a team reporting in Molecular Systems Biology has built a computational framework designed to fix exactly this problem, and in doing so has added something genuinely new: the ability to simulate how perturbing specific genes would redirect cellular dynamics within intact tissue.

The new method, called veloAgent, was developed by Raghavan, Yoon, Fonseca, Li, and Ding and described in a companion News & Views commentary by Jiahao Wu and Suoqin Jin of Wuhan University. At its core, veloAgent takes spliced and unspliced mRNA counts as input and passes them through a variational autoencoder, a type of deep generative neural network that compresses high-dimensional expression data into a compact latent representation. This compression serves two purposes. It reduces the noise and dimensionality that plague raw count data, and it produces a robust summary of each cell’s transcriptional state that downstream models can work with more reliably than with raw counts alone.

From that latent representation, a neural network constructed in conjunction with gene-gene interaction networks predicts kinetic parameters for every cell and every gene: the transcription rate alpha, the splicing rate beta, and the degradation rate gamma. These are the same mechanistic quantities that underlie classical RNA velocity theory, but here they are estimated with the benefit of regulatory structure encoded in known interactions between genes. The initial velocity computed from these parameters is then refined in two stages. The first stage applies velocity smoothing, aligning the directions of transcriptionally similar cells to enforce local coherence and suppress erratic, single-cell noise. The second stage is where veloAgent departs most sharply from its predecessors.

That second stage is an agent-based model, a simulation technique borrowed from fields like epidemiology and economics in which individual entities, or agents, interact according to local rules and collective behavior emerges from those interactions. In veloAgent, each cell is an agent embedded in its spatial microenvironment. The velocity assigned to each cell is adjusted through neighborhood-based weighting that incorporates spatial distance, local cell density, and expression similarity. In practical terms, a cell’s inferred trajectory is constrained by what its actual physical neighbors are doing. Rather than treating spatial information as an auxiliary feature bolted onto the analysis, veloAgent uses it as a primary organizing principle, allowing cell-state transitions to emerge from local interactions rather than being imposed from above. The refined velocity field then supports downstream analyses including spatio-temporal dynamics inference, projection of velocity fields onto low-dimensional spaces and spatial tissue maps, and in silico gene perturbation.

The perturbation capability deserves particular attention because it moves the framework from description toward prediction. Instead of simply altering observed expression values, veloAgent modulates the underlying kinetic parameters directly. Increasing or decreasing alpha for a chosen gene simulates transcriptional activation or repression at the level of the dynamical system itself, after which the induced velocity field is recomputed. The functional impact of each simulated perturbation is quantified by measuring how the directionality of cellular velocity vectors changes, particularly with respect to predefined target states or trajectories. Genes are then ranked by their ability to reorient cellular dynamics toward those targets, yielding a prioritized list of candidate regulators of cell-state transitions. Because the perturbations operate within the learned parameter space, large-scale screening can be performed efficiently without retraining the model, while retaining mechanistic interpretability that purely correlative approaches lack.

The authors validated veloAgent on spatial transcriptomics datasets from multiple platforms and across diverse tissue types. In structured tissues such as the brain, the framework produced trajectories that closely aligned with known anatomical organization, avoiding the inconsistent or anatomically implausible directions that earlier methods generated. In silico perturbation analysis identified key regulatory genes whose simulated modulation reversed progression toward terminal fiber-tract cell states, demonstrating that the model can pinpoint levers that genuinely redirect cellular dynamics. In a breast cancer dataset, veloAgent’s inferred transitions were more consistent with expected tumor progression patterns, correcting misdirected flows produced by competing methods, and further perturbation analysis revealed potential therapeutic targets capable of suppressing malignant progression.

The quantitative benchmarks reinforce the qualitative picture. veloAgent outperformed alternative methods in cross-boundary directionality, fate probability, and velocity confidence, the metrics that capture whether inferred trajectories respect tissue boundaries and how reliably they predict cell fates. Notably, the framework also improved temporal velocity estimation for standard single-cell RNA sequencing data even when no spatial information was available, suggesting that the agent-based refinement and gene-gene regulatory constraints confer benefits that extend beyond the spatial setting. For a field in which velocity estimates have often been fragile and method-dependent, that robustness is a meaningful advance.

The broader significance, as Wu and Jin emphasize in their commentary, is that cellular behavior cannot be understood outside its native environment. Transcriptional state, spatial structure, physical constraint, and local interaction all conspire to determine what a cell becomes. By modeling cells as interacting agents within tissue rather than as independent observations, veloAgent offers a more realistic representation of how cellular processes actually unfold, and it provides a mechanistically grounded platform for simulating interventions before any experiment is run in the laboratory.

Challenges remain, and the commentary is candid about them. Spatial transcriptomics data are frequently collected across multiple tissue sections and experimental conditions, creating batch-correction problems and complicating robust cross-sample comparison of cellular dynamics. Integrating additional modalities such as epigenomics and proteomics would give a fuller picture of the regulatory processes driving cell dynamics. Incorporating causal machine learning frameworks could sharpen both interpretability and predictive power, and directly modeling intercellular signaling, rather than approximating neighborhood effects under spatial constraints, would deepen the mechanistic account of how cells influence one another. Still, the trajectory of the field is clear. The long-term ambition, articulated in recent proposals for building virtual cells with artificial intelligence, is to move beyond descriptive analysis toward predictive models capable of simulating cellular behavior in silico, accelerating therapeutic target discovery and advancing precision medicine. veloAgent represents a concrete step along that path: a tool that not only decodes where cells in a tissue are going, but suggests how researchers might steer them somewhere else.

Subject of Research: Spatially resolved RNA velocity modeling and in silico perturbation of cell-state transitions in tissue

Article Title: Decoding and steering spatially resolved cellular dynamics

Article References: Decoding and steering spatially resolved cellular dynamics. (n.d.). https://doi.org/10.1038/s44320-026-00216-7

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00216-7

Keywords: RNA velocity, spatial transcriptomics, single-cell RNA sequencing, variational autoencoder, agent-based model, cell-state transitions, gene perturbation, computational biology, tumor progression, deep learning, gene regulation, Molecular Systems Biology